13.3.7 Matching Graphs and 3-D Network Descriptions

Jacobus, C.J., Chien, R.T.,
Intermediate-Level Vision: Building Vertex-String-Surface (V-S-S) Graphs,
CGIP(15), No. 4, April 1981, pp. 339-363.
Elsevier DOI BibRef 8104

Umeyama, S.,
An Eigen Decomposition Approach to Weighted Graph Matching Problems,
PAMI(10), No. 5, September 1988, pp. 695-703.
IEEE DOI Matching, Eigen Values. Applied to transformations
See also Least-Squares Estimation of Transformation Parameters Between Two Point Patterns. and articulated objects
See also Parameterized Point Pattern Matching and Its Application to Recognition of Object Families. BibRef 8809

Yang, B., Snyder, W.E., and Bilbro, G.L.,
Matching Oversegmented 3D Images to Models Using Association Graphs,
IVC(7), No. 2, May 1989, pp. 135-143.
Elsevier DOI Region matching. Find cliques in a depth first match. BibRef 8905

Fan, T.J., Medioni, G.G., and Nevatia, R.,
Recognizing 3-D Objects Using Surface Descriptions,
PAMI(11), No. 11, November 1989, pp. 1140-1157.
IEEE DOI BibRef 8911 USC Computer Vision BibRef
And: ICCV88(474-481).
IEEE DOI BibRef
Earlier:
3-D Object Recognition Using Surface Descriptions,
DARPA88(383-397). Recognize Three-Dimensional Objects. Use the surface descriptions to recognize the objects. Find a small set of possible matches to the image graph representation and apply the complete matching program to these. Deals with overlaps and predictions based on partial matches.
See also Segmented Description of 3-D Surfaces. BibRef

Fan, T.J.,
Describing and Recognizing 3-D Objects Using Surface Properties,
Berlin: Springer-Verlag1989. BibRef 8900 BookThe report was: BibRef Ph.D.Thesis (CS), August 1988. BibRef USC_IRISTR-237. BibRef

Shapiro, L.G., Moriarty, J.D.[John D.], Haralick, R.M., and Mulgaonkar, P.G.[Prasanna G.],
Matching Three-Dimensional Objects Using a Relational Paradigm,
PR(17), No. 4, 1984, pp. 385-405.
Elsevier DOI BibRef 8400
And: VPISUCS TR. 1983. Distance Metric. BibRef
And:
Matching Three-Dimensional Models,
PRIP81(534-541). This paper discusses the problems of matching 3-d relational models and input descriptions. A relational distance metric and a feature attribute distance metric are used to divide the models into possibly overlapping clusters. The input is matched only to the models in those clusters that it is similar to. BibRef

Mulgaonkar, P.G.[Prasanna G.], Shapiro, L.G., and Haralick, R.M.,
Matching 'Sticks, Plates, and Blobs' Objects Using Geometric and Relational Constraints,
IVC(2), No. 2, May 1984, pp. 85-98.
Elsevier DOI BibRef 8405
Earlier:
Recognizing Three-Dimensional Objects from Single Perspective Views Using Geometric and Relational Reasoning,
PRIP82(479-484). BibRef
And:
Identification of Man-Made Objects using Geometric and Relational Constraints,
DraftThe model specifies the position of the plates, blobs, and sticks with angles and connections. The actual results are based on what the relations and angles can look like from different views. Generate the guess at the camera position and predict how the object will then look. BibRef

Shapiro, L.G.[Linda G.], Moriarty, J.D.[John D.], Mulgaonkar, P.G.[Prasanna G.], and Haralick, R.M.[Robert M.],
Sticks, Plates, and Blobs: Three-Dimensional Object Representation for Scene Analysis,
AAAI-80(28-31). BibRef 8000

Mulgaonkar, P.G., Shapiro, L.G., and Haralick, R.M.,
Using Rough Relational Models for Geometric Reasoning,
CVWS82(116-124). Represent objects with plates, sticks, and blobs. Use these for matching the input view. Objects are put together with binary relations and triples which specify connections and possibly an angle in the connection. BibRef 8200

Shapiro, L.G., Mulgaonkar, P.G., Moriarity, J.D., Haralick, R.M.,
A Generalized Blob Model for 3D Object Representation,
ConferenceWorkshop on Pictoral Data Description, 1980, pp. 106-116. BibRef 8000

Shapiro, L.G., Haralick, R.M.,
A General Spatial Data Structure,
PRIP78(238-249). BibRef 7800

Henikoff, J.[Jorja], Shapiro, L.G.[Linda G.],
Representative Patterns for Model-Based Matching,
PR(26), No. 7, July 1993, pp. 1087-1098. BibRef 9307
Earlier:
Elsevier DOI
Interesting Patterns for Model-Based Machine Vision,
ICCV90(535-538).
IEEE DOI BibRef

Shapiro, L.G.,
View-Class Representation and Matching of 3D Objects,
VF91(479-493). Determine the correspondence between features in the image and features in a view class of a particular object. This gives pose and identifies the object. BibRef 9100

Shapiro, L.G.,
Structural Shape Description and Matching,
PRIP79(413-420). BibRef 7900

Chen, C.H., and Mulgaonkar, P.G.,
Automatic Vision Programming,
CVGIP(55), No. 2, March 1992, pp. 170-183.
Elsevier DOI Compute the utility of features to use in the matching and generate recognition programs. BibRef 9203

Chen, C.H., Mulgaonkar, P.G.,
CAD-Based Feature Utility Measures for Automatic Vision Programming,
CADBV91(106-114). BibRef 9100

Chen, C.H., and Mulgaonkar, P.G.,
Uncertainty Update and Dynamic Search Window for Model-Based Object Recognition,
CVPR91(692-694).
IEEE DOI BibRef 9100

Mulgaonkar, P.G., Haralick, R.M., Shapiro, L.G.,
A Computational Framework for Hypothesis Based Reasoning and Its Applications to Perspective Analysis,
CAIA84(287-294). BibRef 8400

Lee, H.J.[Hsi-Jian], Lei, W.L.[Wen-Ling],
Region Matching and Depth Finding for 3D Objects in Stereo Aerial Photographs,
PR(23), No. 1-2, 1990, pp. 81-94.
Elsevier DOI Geometric relations only. BibRef 9000

Li, S.Z.,
Toward 3D Vision from Range Images: An Optimization Framework and Parallel Networks,
CVGIP(55), No. 3, May 1992, pp. 231-260.
Elsevier DOI A unified approach based on optimization at all levels. Low-level is estimating curvatures, segmenting these curvature images, match graph structures. BibRef 9205

Li, S.Z.,
Object Recognition from Range Data Prior to Segmentation,
IVC(10), No. 8, October 1992, pp. 566-576.
Elsevier DOI BibRef 9210

Kao, C.Y.[Ching-Yao], Kumara, S.R.T.[Soundar R.T.], and Kasturi, R.[Rangachar],
Extraction of 3D Object Features from CAD Boundary Representation Using the Super Relation Graph Method,
PAMI(17), No. 12, December 1995, pp. 1228-1233.
IEEE DOI BibRef 9512

Cinque, L., Yasuda, D., Shapiro, L.G., Tanimoto, S.L., Allen, B.,
An Improved Algorithm for Relational Distance Graph Matching,
PR(29), No. 2, February 1996, pp. 349-359.
Elsevier DOI BibRef 9602

Allen, R.[Robert], Yasuda, D.[Dean], Tanimoto, S.L.[Steven L.], Shapiro, L.G.[Linda G.], Cinque, L.[Luigi],
A Parallel Algorithm for Graph Matching and Its MASPAR Implementation,
CAMP93(13-18). Parallel Algorithms. BibRef 9300

Caelli, T.M., Osman, E., West, G.A.W.,
3D Shape Matching and Inspection Using Geometric Features and Relational Learning,
CVIU(72), No. 3, December 1998, pp. 340-350.
DOI Link BibRef 9812

Li, S.J.[San-Jiang], Ying, M.S.[Ming-Sheng],
Region Connection Calculus: Its models and composition table,
AI(145), No. 1-2, April 2003, pp. 121-146.
Elsevier DOI 0306
BibRef

Anderson, M.[Michael], McCartney, R.[Robert],
Diagram processing: Computing with diagrams,
AI(145), No. 1-2, April 2003, pp. 181-226.
Elsevier DOI 0306
BibRef

Serratosa, F.[Francesc], Alquézar, R.[René], Sanfeliu, A.[Alberto],
Function-described graphs for modelling objects represented by sets of attributed graphs,
PR(36), No. 3, March 2003, pp. 781-798.
Elsevier DOI 0301
BibRef
Earlier: A1, A3, A2:
Modelling and recognising 3D-objects described by multiple views using function-described graphs,
ICPR02(II: 140-143).
IEEE DOI 0211
BibRef
Earlier: A3, A1, A2:
Clustering of Attributed Graphs and Unsupervised Synthesis of Function-described Graphs,
ICPR00(Vol II: 1022-1025).
IEEE DOI 0009
BibRef
Earlier: A1, A2, A3:
Efficient Algorithms for Matching Attributed Graphs and Function-described Graphs,
ICPR00(Vol II: 867-872).
IEEE DOI 0009

See also new graph matching method for point-set correspondence using the EM algorithm and Softassign, A. BibRef

Serratosa, F.[Francesc], Sanfeliu, A.[Alberto],
Function-described graphs applied to 3D object representation,
CIAP97(I: 701-708).
Springer DOI 9709
BibRef

Todorovic, S.[Sinisa], Nechyba, M.C.[Michael C.],
Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions,
PAMI(27), No. 11, November 2005, pp. 1762-1777.
IEEE DOI 0510
BibRef
Earlier:
Detection of artificial structures in natural-scene images using dynamic trees,
ICPR04(I: 35-39).
IEEE DOI 0409
Segment the image for matching. Captures relations (components). BibRef

Todorovic, S.[Sinisa], Nechyba, M.C.[Michael C.],
Interpretation of complex scenes using dynamic tree-structure Bayesian networks,
CVIU(106), No. 1, April 2007, pp. 71-84.
Elsevier DOI 0704
BibRef
And:
Interpretation of Complex Scenes Using Generative Dynamic-Structure Models,
GenModel04(184).
IEEE DOI 0406
BibRef
Earlier:
Multiresolution linear discriminant analysis: efficient extraction of geometrical structures in images,
ICIP03(I: 1029-1032).
IEEE DOI 0312
Generative models; Bayesian networks; Dynamic trees; Variational inference; Image segmentation; Object recognition BibRef

Todorovic, S.[Sinisa], Ahuja, N.[Narendra],
Region-Based Hierarchical Image Matching,
IJCV(78), No. 1, June 2008, pp. 47-66.
Springer DOI 0803
BibRef
Earlier:
Extracting Subimages of an Unknown Category from a Set of Images,
CVPR06(I: 927-934).
IEEE DOI 0606
BibRef
And: A2, A1:
Learning the Taxonomy and Models of Categories Present in Arbitrary Images,
ICCV07(1-8).
IEEE DOI 0710
Identify properties of the object, learn a model, segment. BibRef

Payet, N.[Nadia], Todorovic, S.[Sinisa],
Hough Forest Random Field for Object Recognition and Segmentation,
PAMI(35), No. 5, May 2013, pp. 1066-1079.
IEEE DOI 1304
BibRef
Earlier:
From contours to 3D object detection and pose estimation,
ICCV11(983-990).
IEEE DOI 1201
BibRef
And:
Scene shape from texture of objects,
CVPR11(2017-2024).
IEEE DOI 1106
BibRef
Earlier:
From a Set of Shapes to Object Discovery,
ECCV10(V: 57-70).
Springer DOI 1009
BibRef
And:
Matching Hierarchies of Deformable Shapes,
GbRPR09(1-10).
Springer DOI 0905
BibRef

Todorovic, S.[Sinisa], Ahuja, N.[Narendra],
Scale-Invariant Region-Based Hierarchical Image Matching,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Ahuja, N.[Narendra], Todorovic, S.[Sinisa],
Connected Segmentation Tree: A joint representation of region layout and hierarchy,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Todorovic, S.[Sinisa], Ahuja, N.[Narendra],
Unsupervised Category Modeling, Recognition, and Segmentation in Images,
PAMI(30), No. 12, December 2008, pp. 2158-2174.
IEEE DOI 0811
BibRef
Earlier:
Learning subcategory relevances for category recognition,
CVPR08(1-8).
IEEE DOI 0806
Solve the problem of finding the common objects in the set of images, learning the structure of the objects, finding the objects in new images. Tree-representation of the images. BibRef

Huang, J.J.[Jih-Jeng], Tzeng, G.H.[Gwo-Hshiung], Ong, C.S.[Chorng-Shyong],
Multidimensional data in multidimensional scaling using the analytic network process,
PRL(26), No. 6, 1 May 2005, pp. 755-767.
Elsevier DOI 0501
Low dimensional representations for relations. BibRef

Tavares, J.M.R.S.[João Manuel R. S.], Bastos, L.F.[Luísa Ferreira],
Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques,
ELCVIA(5), No. 3, 2005, pp. 1-20.
DOI Link 0505
BibRef
Earlier: A2, A1: AMDO04(39-50).
Springer DOI 0505
Graph matching for deformable objects. BibRef

Pinho, R.R.[Raquel Ramos], Tavares, J.M.R.S.[João Manuel R.S.],
Dynamic Pedobarography Transitional Objects by Lagrange's Equation with FEM, Modal Matching and Optimization Techniques,
ICIAR04(II: 92-99).
Springer DOI 0409
BibRef

Pham, T.V.[Thang V.], Smeulders, A.W.M.[Arnold W.M.],
Object recognition with uncertain geometry and uncertain part detection,
CVIU(99), No. 2, August 2005, pp. 241-258.
Elsevier DOI 0506
Given parts, recognize objects. BibRef

Demirci, M.F.[M. Fatih], Shokoufandeh, A.[Ali], Keselman, Y.[Yakov], Bretzner, L.[Lars], Dickinson, S.J.[Sven J.],
Object Recognition as Many-to-Many Feature Matching,
IJCV(69), No. 2, August 2006, pp. 203-222.
Springer DOI 0606
BibRef
Earlier: A1, A2, A5, A3, A4:
Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs,
ECCV04(Vol I: 322-335).
Springer DOI 0405
BibRef
Earlier: A1, A2, A3, A5, A4:
Many-to-Many Matching of Scale-Space Feature Hierarchies Using Metric Embedding,
ScaleSpace03(17-32).
Springer DOI 0310
BibRef

Shokoufandeh, A.[Ali], Keselman, Y.[Yakov], Demirci, M.F.[M. Fatih], Macrini, D.[Diego], Dickinson, S.J.[Sven J.],
Many-to-many feature matching in object recognition: a review of three approaches,
IET-CV(6), No. 6, 2012, pp. 500-513.
DOI Link 1301
BibRef
Earlier:
Many-to-Many Feature Matching in Object Recognition,
CogVis03(107-125).
Springer DOI 0310
BibRef

Dickinson, S.J.[Sven J.], Shokoufandeh, A.[Ali], Keselman, Y.[Yakov], Demirci, M.F.[M. Fatih], Macrini, D.[Diego],
Object Categorization and the Need for Many-to-Many Matching,
DAGM05(501).
Springer DOI 0509
BibRef

Keselman, Y., Shokoufandeh, A., Demirci, M.F., Dickinson, S.J.,
Many-to-many graph matching via metric embedding,
CVPR03(I: 850-857).
IEEE DOI 0307
BibRef

Shokoufandeh, A.[Ali], Bretzner, L.[Lars], Macrini, D.[Diego], Demirci, M.F.[M. Fatih], Jönsson, C.[Clas], Dickinson, S.J.[Sven J.],
The representation and matching of categorical shape,
CVIU(103), No. 2, August 2006, pp. 139-154.
Elsevier DOI 0608
Generic object recognition; Shape categorization; Graph matching; Scale-spaces; Spectral graph theory BibRef

Kuo, C.T.[Chen-Tsung], Cheng, S.C.[Shyi-Chyi],
3D model retrieval using principal plane analysis and dynamic programming,
PR(40), No. 2, February 2007, pp. 742-755.
Elsevier DOI 0611
3D models; 3D model retrieval; Principal plane analysis; Graph matching; Dynamic programming BibRef

Merchán, P.[Pilar], Adán, A.[Antonio],
Exploration trees on highly complex scenes: A new approach for 3D segmentation,
PR(40), No. 7, July 2007, pp. 1879-1898.
Elsevier DOI 0704
3D segmentation; Object extraction; Occluded scenes; Scene analysis; Unstructured 3D data BibRef

Adan, A.[Antonio], Merchan, P.[Pilar], Salamanca, S.[Santiago], Vazquez, A.S., Adan, M., Cerrada, C.,
Objects Layout Graph for 3D Complex Scenes,
ICIP05(III: 433-436).
IEEE DOI 0512
BibRef

Merchan, P.[Pilar], Adan, A.[Antonio], Salamanca, S.[Santiago],
Recognition of Free-Form Objects in Complex Scenes Using DGI-BS Models,
3DPVT06(970-977).
IEEE DOI 0606
BibRef

Adán, A.[Antonio], Adán, M.[Miguel],
Consensus strategy for clustering using RC-images,
PR(47), No. 1, 2014, pp. 402-417.
Elsevier DOI 1310
BibRef
Earlier:
Extracting Understandable 3D Object Groups with Multiple Similarity Metrics,
CIARP12(179-186).
Springer DOI 1209
3D shape representation BibRef

Adán, A.[Antonio], Adán, M.[Miguel], Salamanca, S.[Santiago], Merchán, P.[Pilar],
Using Non Local Features for 3D Shape Grouping,
SSPR08(644-653).
Springer DOI 0812
BibRef

Merchán, P.[Pilar], Adán, A.[Antonio],
Analyzing DGI-BS: Properties and Performance Under Occlusion and Noise,
ACIVS07(60-71).
Springer DOI 0708
BibRef

Mustière, S.[Sébastien], Devogele, T.[Thomas],
Matching Networks with Different Levels of Detail,
GeoInfo(12), No. 4, December 2008, pp. xx-yy.
Springer DOI 0804
BibRef

Emms, D.[David], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Graph matching using the interference of discrete-time quantum walks,
IVC(27), No. 7, 4 June 2009, pp. 934-949.
Elsevier DOI 0904
BibRef
And: Erratum: PR(42), No. 9, September 2009, pp. 2218.
Elsevier DOI 0905
BibRef
Earlier: A1, A3, A2:
Graph drawing using quantum commute time,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier: A1, A3, A2:
A Correspondence Measure for Graph Matching Using the Discrete Quantum Walk,
GbRPR07(81-91).
Springer DOI 0706
BibRef
And: A1, A3, A2:
Graph Similarity Using Interfering Quantum Walks,
CAIP07(823-831).
Springer DOI 0708
BibRef
Earlier: A1, A3, A2:
Graph Matching using Interference of Coined Quantum Walks,
ICPR06(III: 133-136).
IEEE DOI 0609
Graph matching; Discrete time quantum walk; Auxiliary structure; Interference amplitude; Probabilistic model BibRef

Emms, D.[David], Severini, S.[Simone], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Coined Quantum Walks Lift the Cospectrality of Graphs and Trees,
PR(42), No. 9, September 2009, pp. 1988-2002.
Elsevier DOI 0905
BibRef
And: Corrigendum: PR(48), No. 4, 2015, pp. 1574-1575.
Elsevier DOI 1502
BibRef
Earlier: EMMCVPR05(332-345).
Springer DOI 0601
Graph spectra; Quantum walks; Unitary representations; Strongly regular graphs; Graph matching; Cospectrality BibRef

Agathos, A.[Alexander], Pratikakis, I.E.[Ioannis E.], Papadakis, P.[Panagiotis], Perantonis, S.J.[Stavros J.], Azariadis, P.[Philip], Sapidis, N.S.[Nickolas S.],
3D articulated object retrieval using a graph-based representation,
VC(26), No. 10, October 2010, pp. 1301-1319.
WWW Link. 1101
BibRef
Earlier:
Retrieval of 3D Articulated Objects Using A Graph-Based Representation,
3DOR09(29-36)
PDF File.
DOI Link 1301
BibRef

Papadakis, P.[Panagiotis],
A Use-Case Study on Multi-view Hypothesis Fusion for 3D Object Classification,
Multiview17(2446-2452)
IEEE DOI 1802
Feature extraction, Robot sensing systems, Semantics, Shape, BibRef

Xiao, B.[Bai], Song, Y.Z.[Yi-Zhe], Hall, P.M.[Peter M.],
Learning invariant structure for object identification by using graph methods,
CVIU(115), No. 7, July 2011, pp. 1023-1031.
Elsevier DOI 1106
Graph structure; Object recognition; Structure learning; Spectral graph theory BibRef

Wu, Q.[Qi], Cai, H.P.[Hong-Ping], Hall, P.M.[Peter M.],
Learning Graphs to Model Visual Objects across Different Depictive Styles,
ECCV14(VII: 313-328).
Springer DOI 1408
BibRef

Mohamed, W.[Waleed], Ben Hamza, A.,
Reeb graph path dissimilarity for 3D object matching and retrieval,
VC(27), No. 3, March 2011, pp. 305-318.
WWW Link. 1203
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Torresani, L.[Lorenzo], Kolmogorov, V.[Vladimir], Rother, C.[Carsten],
A Dual Decomposition Approach to Feature Correspondence,
PAMI(35), No. 2, February 2013, pp. 259-271.
IEEE DOI 1301
BibRef
Earlier:
Feature Correspondence Via Graph Matching: Models and Global Optimization,
ECCV08(II: 596-609).
Springer DOI
PDF File. 0810
Code, Matching. Code:
WWW Link. BibRef

Feng, W.[Wei], Liu, Z.Q.A.[Zhi-Qi-Ang], Wan, L.[Liang], Pun, C.M.[Chi-Man], Jiang, J.M.[Jian-Min],
A spectral-multiplicity-tolerant approach to robust graph matching,
PR(46), No. 10, October 2013, pp. 2819-2829.
Elsevier DOI 1306
Attributed graph matching; Spectral multiplicity; Spectrum normalization; Graph warping; Shape retrieval BibRef

Lu, K.[Ke], Ji, R.R.[Rong-Rong], Tang, J.H.[Jin-Hui], Gao, Y.[Yue],
Learning-Based Bipartite Graph Matching for View-Based 3D Model Retrieval,
IP(23), No. 10, October 2014, pp. 4553-4563.
IEEE DOI 1410
graph theory BibRef

Liu, A.A., Nie, W.Z., Gao, Y., Su, Y.T.,
Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval,
IP(25), No. 5, May 2016, pp. 2103-2116.
IEEE DOI 1604
computer graphics BibRef

Liu, A.A., Nie, W.Z., Gao, Y., Su, Y.T.,
View-Based 3-D Model Retrieval: A Benchmark,
Cyber(48), No. 3, March 2018, pp. 916-928.
IEEE DOI 1802
Adaptation models, Computational modeling, Feature extraction, Mathematical model, Shape, Solid modeling, Visualization, graph matching BibRef

Nie, W.Z.[Wei-Zhi], Zhao, Y.[Yue], Song, D.[Dan], Gao, Y.[Yue],
DAN: Deep-Attention Network for 3D Shape Recognition,
IP(30), 2021, pp. 4371-4383.
IEEE DOI 2104
Shape, Task analysis, Feature extraction, Correlation, Computer architecture, retrieval BibRef

Xiao, J.W.[Jin-Wen], Wei, H.[Hui],
Scale-invariant contour segment context in object detection,
IVC(32), No. 12, 2014, pp. 1055-1066.
Elsevier DOI 1412
Object detection. Hough framework for scale. Graph based matching. Detect boundary of object. BibRef

Serradell, E.[Eduard], Pinheiro, M.A., Sznitman, R., Kybic, J.[Jan], Moreno-Noguer, F.[Francesc], Fua, P.,
Non-Rigid Graph Registration Using Active Testing Search,
PAMI(37), No. 3, March 2015, pp. 625-638.
IEEE DOI 1502
Gaussian processes BibRef

Serradell, E.[Eduard], Glowacki, P.[Przemyslaw], Kybic, J.[Jan], Moreno-Noguer, F.[Francesc], Fua, P.[Pascal],
Robust non-rigid registration of 2D and 3D graphs,
CVPR12(996-1003).
IEEE DOI 1208
BibRef

Shang, H.L.[Hui-Liang], Tao, Y.D.[Yu-Dong], Gao, Y.[Yuan], Zhang, C.[Chen], Wang, X.L.[Xiao-Ling],
An Improved Invariant for Matching Molecular Graphs Based on VF2 Algorithm,
SMCS(45), No. 1, January 2015, pp. 122-128.
IEEE DOI 1502
bioinformatics BibRef

Wang, C.[Chong], Huang, K.Q.[Kai-Qi],
How to use Bag-of-Words model better for image classification,
IVC(38), No. 1, 2015, pp. 65-74.
Elsevier DOI 1506
Image classification BibRef

Huang, Y.Z.[Yong-Zhen], Huang, K.Q.[Kai-Qi], Wang, C.[Chong], Tan, T.N.[Tie-Niu],
Exploring relations of visual codes for image classification,
CVPR11(1649-1656).
IEEE DOI 1106
bag-of-features links (relations) between features. BibRef

Zhao, X.[Xin], Yu, Y.[Yinan], Huang, Y.Z.[Yong-Zhen], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Feature coding via vector difference for image classification,
ICIP12(3121-3124).
IEEE DOI 1302
BibRef
Earlier: A3, A4, A2, A5, Only:
Salient coding for image classification,
CVPR11(1753-1760).
IEEE DOI 1106
BibRef

Yu, T.S.[Tian-Shu], Wang, R.S.[Rui-Sheng],
Scene parsing using graph matching on street-view data,
CVIU(145), No. 1, 2016, pp. 70-80.
Elsevier DOI 1604
Scene parsing BibRef

Komodakis, N.[Nikos], Pawan Kumar, M., Paragios, N.[Nikos],
Hyper-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision,
FTCGV(10), No. 1, 2016, pp. 1-102.
DOI Link 1606
BibRef

Zhou, F.[Feng], de la Torre, F.[Fernando],
Factorized Graph Matching,
PAMI(38), No. 9, September 2016, pp. 1774-1789.
IEEE DOI 1609
BibRef
Earlier: CVPR12(127-134).
IEEE DOI 1208
Approximation algorithms BibRef

Yang, M.Y.[Michael Ying], Liao, W.T.[Wen-Tong], Ackermann, H.[Hanno], Rosenhahn, B.[Bodo],
On support relations and semantic scene graphs,
PandRS(131), No. 1, 2017, pp. 15-25.
Elsevier DOI 1709
Scene, understanding BibRef

Cong, Y.[Yuren], Ackermann, H.[Hanno], Liao, W.T.[Wen-Tong], Yang, M.Y.[Michael Ying], Rosenhahn, B.[Bodo],
Nodis: Neural Ordinary Differential Scene Understanding,
ECCV20(XX:636-653).
Springer DOI 2011
BibRef

Zhu, J.[Jie], Wu, S.F.[Shu-Fang], Wang, X.Z.[Xi-Zhao], Yang, G.Q.[Guo-Qing], Ma, L.Y.[Li-Yan],
Multi-image matching for object recognition,
IET-CV(12), No. 3, April 2018, pp. 350-356.
DOI Link 1804
Image Graph representation. BibRef

Chang, H.J.[Hyung Jin], Demiris, Y.[Yiannis],
Highly Articulated Kinematic Structure Estimation Combining Motion and Skeleton Information,
PAMI(40), No. 9, September 2018, pp. 2165-2179.
IEEE DOI 1808
Kinematics, Motion segmentation, Estimation, Skeleton, Shape, adaptive kernel selection BibRef

Chang, H.J.[Hyung Jin], Fischer, T.[Tobias], Petit, M.[Maxime], Zambelli, M.[Martina], Demiris, Y.[Yiannis],
Learning Kinematic Structure Correspondences Using Multi-Order Similarities,
PAMI(40), No. 12, December 2018, pp. 2920-2934.
IEEE DOI 1811
BibRef
Earlier:
Kinematic Structure Correspondences via Hypergraph Matching,
CVPR16(4216-4225)
IEEE DOI 1612
Kinematics, Robot sensing systems, Motion segmentation, Image sequences, Humanoid robots, humanoid robotics BibRef

Hong, D.F.[Dan-Feng], Yokoya, N.[Naoto], Chanussot, J.[Jocelyn], Xu, J.[Jian], Zhu, X.X.[Xiao Xiang],
Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction,
PandRS(158), 2019, pp. 35-49.
Elsevier DOI 1912
Dimensionality reduction, Graph learning, Hyperspectral image, Iterative, Label propagation, Multitask regression, Semi-supervised BibRef

Kuang, Z.Z.[Zhen-Zhong], Yu, J.[Jun], Zhu, S.G.[Su-Guo], Li, Z.M.[Zong-Min], Fan, J.P.[Jian-Ping],
Effective 3-D Shape Retrieval by Integrating Traditional Descriptors and Pointwise Convolution,
MultMed(21), No. 12, December 2019, pp. 3164-3177.
IEEE DOI 1912
Shape, Deep learning, Heating systems, Feature extraction, Strain, Kernel, parallel knowledge transfer BibRef

Chen, F.[Feng], Li, B.[Bo], Li, L.[Liang],
3D object retrieval with graph-based collaborative feature learning,
JVCIR(58), 2019, pp. 261-268.
Elsevier DOI 1901
3D Object retrieval, Collaborative feature learning, Hypergraph learning, Bipartite graph matching BibRef

Ma, Y.L.[Yu-Liang], Yuan, Y.[Ye], Liu, M.[Meng], Wang, G.R.[Guo-Ren], Wang, Y.S.[Yi-Shu],
Graph simulation on large scale temporal graphs,
GeoInfo(24), No. 1, January 2020, pp. 199-220.
Springer DOI 2002
BibRef

Kim, U.H., Park, J.M., Song, T.j., Kim, J.H.,
3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents,
Cyber(50), No. 12, December 2020, pp. 4921-4933.
IEEE DOI 2012
Semantics, Intelligent agents, Task analysis, Visualization, Usability, Scalability, Computational modeling, 3-D scene graph, scene understanding BibRef

Golec, K., Palierne, J.F., Zara, F., Nicolle, S., Damiand, G.,
Hybrid 3D mass-spring system for simulation of isotropic materials with any Poisson's ratio,
VC(36), No. 4, April 2020, pp. 809-825.
Springer DOI 2004
BibRef

Bai, J.J.[Jun-Jie], Gong, B.[Biao], Zhao, Y.[Yining], Lei, F.Q.[Fu-Qiang], Yan, C.G.[Cheng-Gang], Gao, Y.[Yue],
Multi-Scale Representation Learning on Hypergraph for 3D Shape Retrieval and Recognition,
IP(30), 2021, pp. 5327-5338.
IEEE DOI 2106
Shape, Feature extraction, Correlation, Convolution, Task analysis, Neural networks, 3D shape retrieval, recognition BibRef

Yu, Y.F.[Yu-Feng], Xu, G.X.[Guo-Xia], Huang, K.K.[Ke-Kun], Zhu, H.[Hu], Chen, L.[Long], Wang, H.[Hao],
Dual Calibration Mechanism Based L2, p-Norm for Graph Matching,
CirSysVideo(31), No. 6, June 2021, pp. 2343-2358.
IEEE DOI 2106
Calibration, Robustness, Strain, Image edge detection, Linear programming, Task analysis, Calibration mechanism, similarity metric BibRef

Liang, Q.[Qi], Li, Q.[Qiang], Zhang, L.[Lihu], Mi, H.X.[Hai-Xiao], Nie, W.Z.[Wei-Zhi], Li, X.[Xuanya],
MHFP: Multi-view based hierarchical fusion pooling method for 3D shape recognition,
PRL(150), 2021, pp. 214-220.
Elsevier DOI 2109
Retrieval, Classification, Recognition, Multi-view, 3D attention BibRef

Yang, J.[Jing], Yang, X.[Xu], Zhou, Z.B.[Zhang-Bing], Liu, Z.Y.[Zhi-Yong],
Graph matching based on fast normalized cut and multiplicative update mapping,
PR(122), 2022, pp. 108228.
Elsevier DOI 2112
Graph matching, Fast normalized cut, Discrete constraint, Multiplicative update BibRef

Sukurdeep, Y.[Yashil], Bauer, M.[Martin], Charon, N.[Nicolas],
A New Variational Model for Shape Graph Registration with Partial Matching Constraints,
SIIMS(15), No. 1, 2022, pp. 261-292.
DOI Link 2204
BibRef

Chen, J.X.[Jia-Xuan], Chen, S.[Shuang], Chen, X.X.[Xiao-Xian], Dai, Y.[Yuan], Yang, Y.[Yang],
CSR-Net: Learning Adaptive Context Structure Representation for Robust Feature Correspondence,
IP(31), 2022, pp. 3197-3210.
IEEE DOI 2205
Task analysis, Feature extraction, Deep learning, Computer architecture, Visualization, Transforms, Image matching, deep learning BibRef

Fuchs, M.[Mathias], Riesen, K.[Kaspar],
A novel way to formalize stable graph cores by using matching-graphs,
PR(131), 2022, pp. 108846.
Elsevier DOI 2208
Graph matching, Matching-graphs, Graph edit distance, Structural pattern recognition BibRef

Doi, K.[Kento], Hamaguchi, R.[Ryuhei], Iwasawa, Y.[Yusuke], Onishi, M.[Masaki], Matsuo, Y.[Yutaka], Sakurada, K.[Ken],
Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Cui, W.[Wei], Hao, Y.J.[Yuan-Jie], Xu, X.[Xing], Feng, Z.Y.[Zhan-Yun], Zhao, H.L.[Hui-Lin], Xia, C.[Cong], Wang, J.[Jin],
Remote Sensing Scene Graph and Knowledge Graph Matching with Parallel Walking Algorithm,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Wang, R.Z.[Run-Zhong], Yan, J.C.[Jun-Chi], Yang, X.K.[Xiao-Kang],
Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach,
PAMI(45), No. 6, June 2023, pp. 6984-7000.
IEEE DOI 2305
BibRef
Earlier:
Learning Combinatorial Embedding Networks for Deep Graph Matching,
ICCV19(3056-3065)
IEEE DOI 2004
Mathematical model, Feature extraction, Peer-to-peer computing, Training, Optimization, Tensors, Pattern matching, Graph matching, combinatorial optimization. computational complexity, graph theory, mathematics computing, supervised learning, structure-wise affinity, matching procedure, Pipelines BibRef

Zhang, Y.X.[Yu-Xuan], Li, Y.Y.X.[Yuan-Yan-Xiang], Wei, X.[Xian], Yang, Y.S.[Yong-Sheng], Liu, L.[Lei], Murphey, Y.L.[Yi Lu],
Graph matching for knowledge graph alignment using edge-coloring propagation,
PR(144), 2023, pp. 109851.
Elsevier DOI 2310
Knowledge graph, Entity alignment, Relation alignment, Quadratic assignment problem BibRef

Gillioz, A.[Anthony], Riesen, K.[Kaspar],
Graph-based pattern recognition on spectral reduced graphs,
PR(144), 2023, pp. 109859.
Elsevier DOI 2310
Graph matching, Graph classification, Graph reduction BibRef

Chen, S.X.[Shun-Xing], Xiao, G.B.[Guo-Bao], Guo, J.W.[Jun-Wen], Wu, Q.Q.[Qiang-Qiang], Ma, J.Y.[Jia-Yi],
DHM-Net: Deep Hypergraph Modeling for Robust Feature Matching,
IP(33), 2024, pp. 6002-6015.
IEEE DOI Code:
WWW Link. 2411
Data models, Computational modeling, Representation learning, Pattern matching, Feature extraction, Deep learning, Topology, correspondence learning BibRef

Eisenberger, M.[Marvin], Toker, A.[Aysim], Leal-Taixè, L.[Laura], Cremers, D.[Daniel],
G-MSM: Unsupervised Multi-Shape Matching with Graph-Based Affinity Priors,
CVPR23(22762-22772)
IEEE DOI 2309

WWW Link. BibRef

Haller, S.[Stefan], Feineis, L.[Lorenz], Hutschenreiter, L.[Lisa], Bernard, F.[Florian], Rother, C.[Carsten], Kainmüller, D.[Dagmar], Swoboda, P.[Paul], Savchynskyy, B.[Bogdan],
A Comparative Study of Graph Matching Algorithms in Computer Vision,
ECCV22(XXIII:636-653).
Springer DOI 2211
BibRef

Yadav, R., Dupé, F.X., Takerkart, S., Auzias, G.,
On The Relevance of Multi-Graph Matching for Sulcal Graphs,
ICIP22(2536-2540)
IEEE DOI 2211
Geometry, Pathology, Neuroscience, Scalability, Sociology, Benchmark testing BibRef

Liu, C.[Chang], Zhang, S.F.[Shao-Feng], Yang, X.K.[Xiao-Kang], Yan, J.C.[Jun-Chi],
Self-supervised Learning of Visual Graph Matching,
ECCV22(XXIII:370-388).
Springer DOI 2211
BibRef

Saleh, M.[Mahdi], Wu, S.C.[Shun-Cheng], Cosmo, L.[Luca], Navab, N.[Nassir], Busam, B.[Benjamin], Tombari, F.[Federico],
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport,
CVPR22(11747-11757)
IEEE DOI 2210
Training, Representation learning, Shape, Pipelines, Pose estimation, Logic gates, Segmentation, grouping and shape analysis, Representation learning BibRef

Hutschenreiter, L.[Lisa], Haller, S.[Stefan], Feineis, L.[Lorenz], Rother, C.[Carsten], Kainmüller, D.[Dagmar], Savchynskyy, B.[Bogdan],
Fusion Moves for Graph Matching,
ICCV21(6250-6259)
IEEE DOI 2203
Approximation algorithms, Markov random fields, Optimization and learning methods BibRef

Chen, Z.X.[Zi-Xuan], Xie, Z.H.[Zhi-Hui], Yan, J.C.[Jun-Chi], Zheng, Y.Q.[Yin-Qiang], Yang, X.K.[Xiao-Kang],
Layered Neighborhood Expansion for Incremental Multiple Graph Matching,
ECCV20(X:251-267).
Springer DOI 2011
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Xu, M.H.[Ming-Hao], Wang, H.[Hang], Ni, B.B.[Bing-Bing], Tian, Q.[Qi], Zhang, W.J.[Wen-Jun],
Cross-Domain Detection via Graph-Induced Prototype Alignment,
CVPR20(12352-12361)
IEEE DOI 2008
Code, Alignment.
WWW Link. Proposals, Prototypes, Task analysis, Detectors, Adaptation models, Training, Merging BibRef

Wang, T., Liu, H., Li, Y., Jin, Y., Hou, X., Ling, H.,
Learning Combinatorial Solver for Graph Matching,
CVPR20(7565-7574)
IEEE DOI 2008
Machine learning, Optimization, Labeling, Approximation algorithms, Buildings, Training, Visualization BibRef

Yu, T.S.[Tian-Shu], Yan, J.C.[Jun-Chi], Liu, W.[Wei], Li, B.X.[Bao-Xin],
Incremental Multi-graph Matching via Diversity and Randomness Based Graph Clustering,
ECCV18(XIII: 142-158).
Springer DOI 1810
BibRef

Sandi, G.[Giulia], Vascon, S.[Sebastiano], Pelillo, M.[Marcello],
On Association Graph Techniques for Hypergraph Matching,
SSSPR18(481-490).
Springer DOI 1810
BibRef

Lang, Y.K.[Yan-Kun], Wu, H.Y.[Hai-Yuan], Chen, Q.[Qian],
A rotation invariant 3D indoor scene labeling approach based on conditional random fields,
ICIP17(600-604)
IEEE DOI 1803
Cameras, Color, Feature extraction, Histograms, Labeling, Radio frequency, 3D point cloud, CRF, rotation invariance BibRef

Carletti, V.[Vincenzo], Foggia, P.[Pasquale], Vento, M.[Mario],
VF2 Plus: An Improved version of VF2 for Biological Graphs,
GbRPR15(168-177).
Springer DOI 1511
BibRef

Huang, S.[Shao], Wang, W.Q.[Wei-Qiang],
Retrieving images combining saliency detection with IRM,
ICIP15(517-521)
IEEE DOI 1512
Center-surround comparison BibRef

Huang, S.[Shao], Wang, W.Q.[Wei-Qiang], Zhang, H.[Hui],
Retrieving images using saliency detection and graph matching,
ICIP14(3087-3091)
IEEE DOI 1502
Computer vision BibRef

Wang, C.[Chao], Wang, L.[Lei], Liu, L.Q.[Ling-Qiao],
Progressive Mode-Seeking on Graphs for Sparse Feature Matching,
ECCV14(II: 788-802).
Springer DOI 1408
BibRef

Collins, T.[Toby], Mesejo, P.[Pablo], Bartoli, A.E.[Adrien E.],
An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions,
ECCV14(VII: 138-153).
Springer DOI 1408
BibRef

Liu, F.[Fang], Liu, Y.[Yang], Zhou, G.Y.[Guang-You], Liu, K.[Kang], Zhao, J.[Jun],
Determining Relation Semantics by Mapping Relation Phrases to Knowledge Base,
ACPR13(420-424)
IEEE DOI 1408
Web sites BibRef

Wang, C.[Chao], Wang, L.[Lei], Liu, L.Q.[Ling-Qiao],
Improving Graph Matching via Density Maximization,
ICCV13(3424-3431)
IEEE DOI 1403
BibRef

Kapec, P.[Peter], Paprcka, M.[Michal], Pažitnaj, A.[Adam],
Intelligent 3D Graph Exploration with Time-Travel Features,
ICCVG12(113-120).
Springer DOI 1210
BibRef

Cho, M.S.[Min-Su], Lee, K.M.[Kyoung Mu],
Progressive graph matching: Making a move of graphs via probabilistic voting,
CVPR12(398-405).
IEEE DOI 1208
BibRef

Bang, Y.[Yoonsik], Ga, C.[Chillo], Yu, K.[Kiyun],
An Iterative Process for Matching Network Data Sets with Different Level of Detail,
GEOBIA10(xx-yy).
PDF File. 1007
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Hashimoto, M.[Marcelo], Cesar, R.M.[Roberto M.],
Object Detection by Keygraph Classification,
GbRPR09(223-232).
Springer DOI 0905
Variation on key-point detection. BibRef

Knossow, D.[David], Sharma, A.[Avinash], Mateus, D.[Diana], Horaud, R.[Radu],
Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors,
GbRPR09(144-153).
Springer DOI 0905
BibRef

Zhang, W.[Wei], Dietterich, T.G.[Thomas G.],
Learning visual dictionaries and decision lists for object recognition,
ICPR08(1-4).
IEEE DOI 0812
Dictionaries map unordered bags of features to know objects. BibRef

Tsolakis, A.[Angelos], Falelakis, M.[Manolis], Delopoulos, A.[Anastasios],
A framework for efficient correspondence using feature interrelations,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Krüger, D.[Daniela], Buschmann, C.[Carsten], Fischer, S.[Stefan],
Location-Free Object Tracking on Graph Structures,
SSC08(99-111).
Springer DOI 0810
BibRef

Hedau, V.[Varsha], Arora, H.[Himanshu], Ahuja, N.[Narendra],
Matching images under unstable segmentations,
CVPR08(1-8).
IEEE DOI 0806
Region matching issues. BibRef

Nwogu, I.[Ifeoma], Corso, J.J.[Jason J.],
(BP)2: Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies,
CVPR08(1-8).
IEEE DOI 0806
BibRef
And:
Labeling Irregular Graphs with Belief Propagation,
IWCIA08(xx-yy).
Springer DOI 0804
BibRef

Corso, J.J.[Jason J.], Yuille, A.L.[Alan L.], Tu, Z.W.[Zhuo-Wen],
Graph-shifts: Natural image labeling by dynamic hierarchical computing,
CVPR08(1-8).
IEEE DOI 0806
BibRef
Earlier: A1, A3, A2:
MRF Labeling with a Graph-Shifts Algorithm,
IWCIA08(xx-yy).
Springer DOI 0804
BibRef

Chen, A.Y.C.[Albert Y.C.], Corso, J.J.[Jason J.],
On the Effects of Normalization in Adaptive MRF Hierarchies,
CompIMAGE10(275-286).
Springer DOI 1006
BibRef

Giro, X., Marques, F.,
Detection of Semantic Objects Using Description Graphs,
ICIP05(I: 1201-1204).
IEEE DOI 0512
BibRef

Li, Y.[Yan], Tsin, Y.H.[Yang-Hai], Genc, Y.[Yakup], Kanade, T.[Takeo],
Flexible Edge Arrangement Templates for Object Detection,
WACV08(1-8).
IEEE DOI 0801
BibRef
Earlier:
Statistical Shape Models for Object Recognition and Part Localization,
BMVC06(II:699).
PDF File. 0609
BibRef
Earlier:
Object Detection Using 2D Spatial Ordering Constraints,
CVPR05(II: 711-718).
IEEE DOI 0507
BibRef
And: CVPR05(II: 1188).
IEEE DOI 0507
Use features:
See also Distinctive Image Features from Scale-Invariant Keypoints. Find featrues, then groupings for match. Part-based recognition. BibRef

Conte, D., Foggia, P., Sansone, C., Vento, M.,
Graph matching applications in pattern recognition and image processing,
ICIP03(II: 21-24).
IEEE DOI 0312
BibRef

Basu, S., Gupta, A., Sarkar, N., Majumder, D.D.,
Knowledge representation for vision: an associative network for single object representation and recognition,
ICPR90(I: 297-299).
IEEE DOI 9006
BibRef

Granlund, G.H., Knuttson, H.,
Compact associative representation of visual information,
ICPR90(II: 200-207).
IEEE DOI 9208
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Spatial Information and Features, Visual Relationships .